SHAP(Shapley Additive explanations) can provide not only the importance of the features but also
show how much each feature contributes, either positively or negatively, to the target variable.
It is a method to explain each individual prediction. This idea of SHAP value comes from the Shapley value in game theory. Shapley value tells how to fairly distribute the contributions among the features, which is the marginal contributions for each feature[1]}.
